MiRNA (microRNA)‐disease association prediction has essential applications for early disease screening. The process of traditional biological experimental validation is both time‐consuming and expensive. However, as artificial intelligence technology continues to advance, computational methods have become efficient tools for predicting miRNA‐disease associations. These methods often rely on the combination of multiple sources of association data and require improved feature mining. This study proposes a dynamic graph attention‐based association prediction model, DGAMDA, which combines feature mapping and dynamic graph attention mechanisms through feature mining on a single miRNA‐disease association network. DGAMDA effectively solves the problems of feature heterogeneity and inadequate feature mining by previous static graph attention mechanisms and achieves high‐precision feature mining and association scoring prediction. We conducted a five‐fold cross‐validation experiment and obtained the mean values of Accuracy, Precision, Recall, and F1‐score, which were .8986, .8869, .9115, and .8984, respectively. Our proposed model outperforms other advanced models in terms of experimental results, demonstrating its effectiveness in feature mining and association prediction based on a single association network. In addition, our model can also be used to predict miRNAs associated with unknown diseases.

中文翻译：

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DGAMDA：基于动态图注意网络预测 miRNA 疾病关联

miRNA (microRNA) 疾病关联预测对于早期疾病筛查具有重要的应用。传统的生物实验验证过程既耗时又昂贵。然而，随着人工智能技术的不断进步，计算方法已成为预测 miRNA 与疾病关联的有效工具。这些方法通常依赖于多个关联数据源的组合，并且需要改进的特征挖掘。本研究提出了一种基于动态图注意力的关联预测模型 DGAMDA，该模型通过在单个 miRNA 疾病关联网络上进行特征挖掘，将特征映射和动态图注意力机制结合起来。DGAMDA有效解决了以往静态图注意力机制存在的特征异构性和特征挖掘不充分的问题，实现了高精度的特征挖掘和关联评分预测。我们进行了五重交叉验证实验，获得了准确率、精确率、召回率和 F1 分数的平均值，分别为 0.8986、0.8869、0.9115 和 0.8984。我们提出的模型在实验结果方面优于其他先进模型，证明了其在基于单个关联网络的特征挖掘和关联预测方面的有效性。此外，我们的模型还可用于预测与未知疾病相关的 miRNA。